Recent posts by Mona

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Data drift, concept drift, and how to monitor for them

Data drift, concept drift, and how to monitor for them

Data and concept drift are frequently mentioned in the context of machine learning model monitoring, but what exactly are they and how are they detected? Furthermore, given the common misconceptions surrounding them, are data and concept drift things to be avoided at all costs or natural and acceptable consequences of training models in production? Read on to find out. In this article we will provide a granular breakdown of model drift, along with methods for detecting them and best practices for dealing with them when you do.

Mona expands monitoring capabilities for intelligent automation processes

Mona expands monitoring capabilities for intelligent automation processes

Mona is happy to announce the expansion of use cases supported by the intelligent monitoring platform for AI / ML to include Robotic Process Automation (“RPA”) workflows. Currently providing customers across 8 different industries with actionable insights into their AI systems, Mona excels at providing complete process visibility, detecting issues within specific segments of data. As a highly extensible platform for many use cases including machine learning, NLU/NLP, speech recognition, and vision, the extension to support intelligent automations and RPA is seamless.  

The three must haves for machine learning monitoring

The three must haves for machine learning monitoring

Monitoring is critical to the success of machine learning models deployed in production systems. Because ML models are not static pieces of code but, rather, dynamic predictors which depend on data, hyperparameters, evaluation metrics, and many other variables, it is vital to have insight into the training, validation, deployment, and inference processes in order to prevent model drift and predictive stasis, and a host of additional issues. However, not all monitoring solutions are created equal. In this post, I highlight three must-haves for machine learning monitoring, which hopefully serve you well whether you are deciding to build or buy a solution.